Barbara Pes
2018-12-10 18:18:55 UTC
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Future Generation Computer Systems (Elsevier)
Special Issue on "Data Exploration in the Web 3.0 Age"
https://www.journals.elsevier.com/future-generation-computer-systems/call-for-papers/special-issue-on-data-exploration-in-the-web-30-age
Deadline for manuscript submissions: JANUARY 20, 2019
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Currently emerging Web 3.0 environments have provided a strong potential for the integration of data sources, applications and tools.
In such a pervasive and highly dynamic scenario, existing techniques for accessing and managing web content
seem to be actually inadequate to satisfy the user needs and more automatic ways of exploring,
joining and sharing information are needed to improve the usability of web resources.
This raises several important challenges for future data and web mining methods.
Such challenges range from the analysis of poorly structured information, such as annotations and tags,
to the provision of intelligent methods that support users in searching and integrating information offered by web resources.
The overall goal of these challenges is not limited to enhance information retrieval but also includes
exploiting the enriched semantics a dataset acquires when used in conjunction with other sources of information.
The synergy of different technologies, including semantic web, natural language search, machine learning,
recommendation agents and artificial intelligence, can be especially fruitful in this perspective.
Furthermore, in the era of big data and Internet of things, we are increasingly dealing with a huge amount of information
generated by heterogeneous sources. Indeed, almost every individual leaves digital traces when interacting with sensor networks,
cloud services and positioning services, through a variety of mobile devices and smart objects.
A growing attention is thus devoted to the design of suitable approaches for exploring this kind of data,
in order to extract actionable knowledge about people, things, and their interactions.
More generally, the dimensionality and the complexity of gathered data is fast increasing in almost all applications domains,
giving rise to the need of innovative data analysis approaches.
The goal of this FGCS special issue is to foster the dissemination of top-notch results in all the areas related to Data Exploration
in a very broad sense, including contributions from data mining, query languages, semantic analysis, data visualization,
graph databases and other fields related to the analysis and exploitation of data.
TOPICS OF INTEREST
Potential topics include, but are not limited to:
- Text and data mining, knowledge discovery
- Faceted search and browsing
- Information retrieval
- Data visualization and ux for web 3.0 data
- Querying interfaces and languages including constrained natural languages
- Entity recognition and merging, type classification, record linkage and property ranking
- Privacy and security issues in data exploration
- Recommendation agents and artificial intelligence technologies
- High-dimensional data analysis
- Machine learning and statistical methods for data analysis and processing
- Natural language processing for data extraction
- Platforms and applications exploring data in all domains including social, web, bioinformatics and finance
- Knowledge graph creation, reasoning, and exploration
- Data streams and the internet of things
- Semantic web and linked data analytics
GUEST EDITORS
Letizia Tanca, DEIB-Politecnico di Milano, Italy
Georgia Koutrika, Athena Research Center, Greece
Maurizio Atzori, University of Cagliari, Italy
Barbara Pes, University of Cagliari, Italy
_______________________________________________
Please do not post msgs that are not relevant to the database community at large. Go to www.cs.wisc.edu/dbworld for guidelines and posting forms.
To unsubscribe, go to https://lists.cs.wisc.edu/mailman/listinfo/dbworld
Future Generation Computer Systems (Elsevier)
Special Issue on "Data Exploration in the Web 3.0 Age"
https://www.journals.elsevier.com/future-generation-computer-systems/call-for-papers/special-issue-on-data-exploration-in-the-web-30-age
Deadline for manuscript submissions: JANUARY 20, 2019
****************************************************************************
Currently emerging Web 3.0 environments have provided a strong potential for the integration of data sources, applications and tools.
In such a pervasive and highly dynamic scenario, existing techniques for accessing and managing web content
seem to be actually inadequate to satisfy the user needs and more automatic ways of exploring,
joining and sharing information are needed to improve the usability of web resources.
This raises several important challenges for future data and web mining methods.
Such challenges range from the analysis of poorly structured information, such as annotations and tags,
to the provision of intelligent methods that support users in searching and integrating information offered by web resources.
The overall goal of these challenges is not limited to enhance information retrieval but also includes
exploiting the enriched semantics a dataset acquires when used in conjunction with other sources of information.
The synergy of different technologies, including semantic web, natural language search, machine learning,
recommendation agents and artificial intelligence, can be especially fruitful in this perspective.
Furthermore, in the era of big data and Internet of things, we are increasingly dealing with a huge amount of information
generated by heterogeneous sources. Indeed, almost every individual leaves digital traces when interacting with sensor networks,
cloud services and positioning services, through a variety of mobile devices and smart objects.
A growing attention is thus devoted to the design of suitable approaches for exploring this kind of data,
in order to extract actionable knowledge about people, things, and their interactions.
More generally, the dimensionality and the complexity of gathered data is fast increasing in almost all applications domains,
giving rise to the need of innovative data analysis approaches.
The goal of this FGCS special issue is to foster the dissemination of top-notch results in all the areas related to Data Exploration
in a very broad sense, including contributions from data mining, query languages, semantic analysis, data visualization,
graph databases and other fields related to the analysis and exploitation of data.
TOPICS OF INTEREST
Potential topics include, but are not limited to:
- Text and data mining, knowledge discovery
- Faceted search and browsing
- Information retrieval
- Data visualization and ux for web 3.0 data
- Querying interfaces and languages including constrained natural languages
- Entity recognition and merging, type classification, record linkage and property ranking
- Privacy and security issues in data exploration
- Recommendation agents and artificial intelligence technologies
- High-dimensional data analysis
- Machine learning and statistical methods for data analysis and processing
- Natural language processing for data extraction
- Platforms and applications exploring data in all domains including social, web, bioinformatics and finance
- Knowledge graph creation, reasoning, and exploration
- Data streams and the internet of things
- Semantic web and linked data analytics
GUEST EDITORS
Letizia Tanca, DEIB-Politecnico di Milano, Italy
Georgia Koutrika, Athena Research Center, Greece
Maurizio Atzori, University of Cagliari, Italy
Barbara Pes, University of Cagliari, Italy
_______________________________________________
Please do not post msgs that are not relevant to the database community at large. Go to www.cs.wisc.edu/dbworld for guidelines and posting forms.
To unsubscribe, go to https://lists.cs.wisc.edu/mailman/listinfo/dbworld